Hybrid overactuated tilt rotor uncrewed aerial vehicles (TRUAVs) are a category of versatile UAVs known for their exceptional wind resistance capabilities. However, their extensive operational range, combined with thrust vectoring capabilities, presents complex control challenges due to nonaffine dynamics and the necessity to coordinate lift and thrust for controlling accelerations at varying airspeeds. Traditionally, these vehicles rely on switched logic controllers with two or more intermediate states to control transitions. In this study, we introduce an innovative, unified incremental nonlinear controller designed to seamlessly control an overactuated dual-axis tilting rotor quad-plane throughout its entire flight envelope. Our controller is based on an incremental nonlinear control allocation algorithm to simultaneously generate pitch and roll commands, along with physical actuator commands. The control allocation problem is solved using a sequential quadratic programming (SQP) iterative optimization algorithm making it well-suited for the nonlinear actuator effectiveness typical of thrust vectoring vehicles. The controller's design integrates desired roll and pitch angle inputs. These desired attitude angles are managed by the controller and then conveyed to the vehicle during slow airspeed phases, when the vehicle maintains its 6-degrees of freedom (6-DOF). As the airspeed increases, the controller seamlessly shifts its focus to generating attitude commands for lift production, consequently smoothly disregarding the desired roll and pitch angles. Furthermore, our controller integrates an angle of attack (AoA) protection logic to mitigate wing stalling risks during transitions. It also features a yaw rate reference model to enable coordinated turns and minimize side-slip. The effectiveness of our proposed control technique has been confirmed through comprehensive flight tests. These tests demonstrated the successful transition from hovering flight to forward flight, the attainment of vertical and lateral accelerations, and the ability to revert to hovering.

This paper presents a method to recover quadrotor Unmanned Air Vehicles (UAVs) from a throw, when no control parameters are known before the throw. We leverage the availability of high-frequency rotor speed feedback available in racing drone hardware and software to find control effectiveness values and fit a motor model using recursive least squares (RLS) estimation. Furthermore, we propose an excitation sequence that provides large actuation commands while guaranteeing to stay within gyroscope sensing limits. After 450ms of excitation, an Incremental Nonlinear Dynamic Inversion (INDI) attitude controller uses the 52 fitted parameters to arrest rotational motion and recover an upright attitude. Finally, a Nonlinear Dynamic Inversion (NDI) position controller drives the craft to a position setpoint. The proposed algorithm runs efficiently on microcontrollers found in common UAV flight controllers, and was shown to recover an agile quadrotor every time in live experiments with as low as 3.5m throw height, demonstrating robustness against initial rotations and noise. We also demonstrate control of randomized quadrotors in simulated throws, where the parameter fitting Root-Mean-Square (RMS) error is typically within 10% of the true value.

We present a novel controller for fixed-wing UAVs that enables autonomous soaring in an orographic wind field, extending flight endurance. Our method identifies soaring regions and addresses position control challenges by introducing a target gradient line (TGL) on which the UAV achieves an equilibrium soaring position, where sink rate and updraft are balanced. Experimental testing validates the controller's effectiveness in maintaining autonomous soaring flight without using any thrust in a non-static wind field. We also demonstrate a single degree of control freedom in a soaring position through manipulation of the TGL.

Utilizing wind hovering techniques of soaring birds can save energy expenditure and improve the flight endurance of micro air vehicles (MAVs). Here, we present a novel method for fully autonomous orographic soaring without a priori knowledge of the wind field. Specifically, we devise an Incremental Nonlinear Dynamic Inversion (INDI) controller with control allocation, adapting it for autonomous soaring. This allows for both soaring and the use of the throttle if necessary, without changing any gain or parameter during the flight. Furthermore, we propose a simulated-annealing-based optimization method to search for soaring positions. This enables for the first time an MAV to autonomously find a feasible soaring position while minimizing throttle usage and other control efforts. Autonomous orographic soaring was performed in the wind tunnel. The wind speed and incline of a ramp were changed during the soaring flight. The MAV was able to perform autonomous orographic soaring for flight times of up to 30 minutes. The mean throttle usage was only 0.25% for the entire soaring flight, whereas normal powered flight requires 38%. Also, it was shown that the MAV can find a new soaring spot when the wind field changes during the flight.

One of the emerging tasks for Micro Air Vehicles (MAVs) is autonomous indoor navigation. While commonly employed platforms for such tasks are micro-quadrotors, insect-inspired flapping wing MAVs can offer many advantages, such as being inherently safe due to their low inertia, reciprocating wings bouncing of objects or potentially lower noise levels compared to rotary wings. Here, we present the first flapping wing MAV to perform an autonomous multi-room exploration task. Equipped with an on-board autopilot and a 4 g stereo vision system, the DelFly Explorer succeeded in combining the two most common tasks of an autonomous indoor exploration mission: room exploration and door passage. During the room exploration, the vehicle uses stereo-vision based droplet algorithm to avoid and navigate along the walls and obstacles. Simultaneously, it is running a newly developed monocular color based Snake-gate algorithm to locate doors. A successful detection triggers the heading-based door passage algorithm. In the real-world test, the vehicle could successfully navigate, multiple times in a row, between two rooms separated by a corridor, demonstrating the potential of flapping wing vehicles for autonomous exploration tasks.

The development of autonomous lightweight MAVs, capable of navigating in unknown indoor environments, is one of the major challenges in robotics. The complexity of this challenge comes from constraints on weight and power consumption of onboard sensing and processing devices. In this paper, we propose the “Droplet” strategy, an avoidance strategy based on stereo vision inputs that outperforms reactive avoidance strategies by allowing constant speed maneuvers while being computationally extremely efficient, and which does not need to store previous images or maps. The strategy deals with nonholonomic motion constraints of most fixed and flapping wing platforms, and with the limited field-of-view of stereo camera systems. It guarantees obstacle-free flight in the absence of sensor and motor noise. We first analyze the strategy in simulation, and then show its robustness in real-world conditions by implementing it on a 20-gram flapping wing MAV.

Autonomous flight of pocket drones is challenging due to the severe limitations on on-board energy, sensing, and processing power. However, tiny drones have great potential as their small size allows maneuvering through narrow spaces while their small weight provides significant safety advantages. This paper presents a computationally efficient algorithm for determining optical flow, which can be run on an STM32F4 microprocessor (168 MHz) of a 4 gram stereo-camera. The optical flow algorithm is based on edge histograms. We propose a matching scheme to determine local optical flow. Moreover, the method allows for sub-pixel flow determination based on time horizon adaptation. We demonstrate velocity measurements in flight and use it within a velocity control-loop on a pocket drone.

Despite an intensive research on flapping flight and flapping wing MAVs in recent years, there are still no accurate models of flapping flight dynamics. This is partly due to lack of free flight data, in particular during manoeuvres. In this work, we present, for the first time, a comparison of free flight forces estimated using solely an on-board IMU with wind tunnel measurements. The IMU based estimation brings higher sampling rates and even lower variation among individual wingbeats, compared to what has been achieved with an external motion tracking system in the past. A good match was found in comparison to wind tunnel measurements; the slight differences observed are attributed to clamping effects. Further insight was gained from the on-board rpm sensor, which showed motor speed variation of ± 15% due to load variation over a wingbeat cycle. The IMU based force estimation represents an attractive solution for future studies of flapping wing MAVs as, unlike wind tunnel measurements, it allows force estimation at high temporal resolutions also during manoeuvres.

The autonomous capabilities of light-weight Flapping Wing Micro Air Vehicles (FWMAVs) have much to gain from onboard state estimation and attitude control. In this article, we present the first FWMAV with robust onboard state estimation and attitude control. The tailed FWMAV DelFly II was used, with the main goal to achieve active stabilization in the (passively unstable) hover condition. The attitude is estimated using an Inertial Measurement Unit with a gyroscope, accelerometer and magnetometer and the altitude is estimated using a barometer. A major challenge lies in the disturbance of the accelerometer measurements by the flapping motion of the wings. We propose a mechanical damping mechanism and flap-cycle based filtering to resolve this issue. The pitch estimates have a mean error of 1.5° with respect to the ground-truth measurement from a motion capture system. Using the onboard pitch estimate we can control the attitude of the FWMAV in the forward flight regime with a 30% lower standard deviation than in a trimmed flight. With a different set of gains, the FWMAV is able to perform a hovering flight - showing that a tailed FWMAV has enough control authority for this task. In a fully autonomous hover experiment, the DelFly II stays within a sphere of 0.75 m radius.

We introduce a novel setup of self-supervised learning (SSL), in which optical flow provides the supervised outputs. Optical flow requires significant movement for obstacle detection. The main advantage of the introduced method is that after learning, a robot can detect obstacles without moving - reducing the risk of collisions in narrow spaces. We investigate this novel setup of SSL in the context of a Micro Air Vehicle (MAV) that needs to select a suitable landing place. Initially, when the MAV flies over a potential landing area, the optical flow processing estimates a ‘surface roughness’ measure, capturing whether there are obstacles sticking out of the landing surface. This measure allows the MAV to select a safe landing place and then land with other optical flow measures such as the divergence. During flight, SSL takes place. For each image a texton distribution is extracted (capturing the visual appearance of the landing surface in sight), and mapped to the current roughness value by a linear regression function. We first demonstrate this principle to work with offline tests involving images captured on board an MAV, and then demonstrate the principle in flight. The experiments show that the MAV can land safely on the basis of optical flow. After learning it can also successfully select safe landing spots in hover. It is even shown that the appearance learning allows the pixel-wise segmentation of obstacles.

Autonomous flight of Flapping Wing Micro Air Vehicles (FWMAVs) is a major challenge in the field of robotics, due to their light weight and the flapping-induced body motions. In this article, we present the first FWMAV with onboard vision processing for autonomous flight in generic environments. In particular, we introduce the DelFly `Explorer', a 20-gram FWMAV equipped with a 0.98-gram autopilot and a 4.0-gram onboard stereo vision system. We explain the design choices that permit carrying the extended payload, while retaining the DelFly's hover capabilities. In addition, we introduce a novel stereo vision algorithm, LongSeq, designed specifically to cope with the flapping motion and the desire to attain a computational effort tuned to the frame rate. The onboard stereo vision system is illustrated in the context of an obstacle avoidance task in an environment with sparse obstacles.

The appearance variation cue captures the variation in texture in a single image. Its use for obstacle avoidance is based on the assumption that there is less such variation when the camera is close to an obstacle. For videos of approaching frontal obstacles, it is demonstrated that combining the cue with optic flow leads to better performance than using either cue alone. In addition, the cue is successfully used to control the 16-g flapping-wing micro air vehicle DelFly II.